Lidar sampling for large-area forest characterization: A review
March, 2012. Michael A. Wulder, Joanne C. White, Ross F. Nelson, Erik Næsset, Hans Ole Ørka, Nicholas C. Coops, Thomas Hilker, Christopher W. Baterd, Terje Gobakken. Remote Sensing of Environment. Volume 121, June 2012, Pages 196–209
The ability to use digital remotely sensed data for forest inventory is often limited by the nature of the measures, which, with the exception of multi-angular or stereo observations, are largely insensitive to vertically distributed attributes. As a result, empirical estimates are typically made to characterize attributes such as height, volume, or biomass, with known asymptotic relationships as signal saturation occurs. Lidar (light detection and ranging) has emerged as a robust means to collect and subsequently characterize vertically distributed attributes. Lidar has been established as an appropriate data source for forest inventory purposes; however, large area monitoring and mapping activities with lidar remain challenging due to the logistics, costs, and data volumes involved.
The use of lidar as a sampling tool for large-area estimation may mitigate some or all of these problems. A number of factors drive, and are common to, the use of airborne profiling, airborne scanning, and spaceborne lidar systems as sampling tools for measuring and monitoring forest resources across areas that range in size from tens of thousands to millions of square kilometers. In this communication, the authors present the case for lidar sampling as a means to enable timely and robust large-area characterizations. They briefly outline the nature of different lidar systems and data, followed by the theoretical and statistical underpinnings for lidar sampling. Current applications are presented and the future potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring is presented. They also include recommendations regarding statistics, lidar sampling schemes, applications (including data integration and stratification), and subsequent information generation.
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Keywords: m. MRV, Other Publications, Extrapolation, Forest, Large area, LiDAR, Light detection and ranging, monitoring, MRV, sampling, satellite, stratification